Topic Modeling in Natural Language Processing
Subhashini Gupta, Grishma Sharma · Zenodo (CERN European Organization for Nuclear Research) · 2021
Every day large quantities of data are collected. As more information is available, the access to what we are seeking gets challenging. We, therefore, require processes and techniques for organizing, searching, and understanding massive amounts of information. The task of topic modeling is to analyze the whole document to learn the meaningful pattern that exists in the document. It is a supervised strategy used to identify and monitor words in clusters of texts (known as the "topics"). Through the use of topic analysis models, companies can load tasks on machines rather than burden employees with too much data. In this paper, we have used Word embedding for Topic Modelling to learn the meaningful pattern of words, and k-means clustering is used to group the words that belong to one group. In this paper, we have created the nine clusters of words from the headline dataset. One of the applications of topic modeling i.e sentiment analysis using the VADER algorithm is also demonstrated in this paper.